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Schenck CH. REM sleep behaviour disorder (RBD): Personal perspectives and research priorities. J Sleep Res 2024:e14228. [PMID: 38782758 DOI: 10.1111/jsr.14228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 04/08/2024] [Accepted: 04/16/2024] [Indexed: 05/25/2024]
Abstract
The formal identification and naming of rapid eye movement (REM) sleep behaviour disorder (RBD) in 1985-1987 is described; the historical background of RBD from 1966 to 1985 is briefly discussed; and RBD milestones are presented. Current knowledge on RBD is identified with reference to recent comprehensive reviews, allowing for a focus on research priorities for RBD: factors and predictors of neurodegenerative phenoconversion from isolated RBD and patient enrolment in neuroprotective trials; isolated RBD clinical research cohorts; epidemiology of RBD; traumatic brain injury, post-traumatic stress disorder, RBD and neurodegeneration; depression, RBD and synucleinopathy; evolution of prodromal RBD to neurodegeneration; gut microbiome dysbiosis and colonic synuclein histopathology in isolated RBD; other alpha-synuclein research in isolated RBD; narcolepsy-RBD; dreams and nightmares in RBD; phasic REM sleep in isolated RBD; RBD, periodic limb movements, periodic limb movement disorder pseudo-RBD; other neurophysiology research in RBD; cardiac scintigraphy (123I-MIBG) in isolated RBD; brain magnetic resonance imaging biomarkers in isolated RBD; microRNAs as biomarkers in isolated RBD; actigraphic, other automated digital monitoring and machine learning research in RBD; prognostic counselling and ethical considerations in isolated RBD; and REM sleep basic science research. RBD research is flourishing, and is strategically situated at an ever-expanding crossroads of clinical (sleep) medicine, neurology, psychiatry and neuroscience.
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Affiliation(s)
- Carlos H Schenck
- Minnesota Regional Sleep Disorders Center, Department of Psychiatry, Hennepin County Medical Center and University of Minnesota Medical School, Minneapolis, Minnesota, USA
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2
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Pinto S, Cardoso R, Atkinson-Clement C, Guimarães I, Sadat J, Santos H, Mercier C, Carvalho J, Cuartero MC, Oliveira P, Welby P, Frota S, Cavazzini E, Vigário M, Letanneux A, Cruz M, Brulefert C, Desmoulins M, Martins IP, Rothe-Neves R, Viallet F, Ferreira JJ. Do Acoustic Characteristics of Dysarthria in People With Parkinson's Disease Differ Across Languages? JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024:1-20. [PMID: 38754039 DOI: 10.1044/2024_jslhr-23-00525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
PURPOSE Cross-language studies suggest more similarities than differences in how dysarthria affects the speech of people with Parkinson's disease (PwPD) who speak different languages. In this study, we aimed to identify the relative contribution of acoustic variables to distinguish PwPD from controls who spoke varieties of two Romance languages, French and Portuguese. METHOD This bi-national, cross-sectional, and case-controlled study included 129 PwPD and 124 healthy controls who spoke French or Portuguese. All participants underwent the same clinical examinations, voice/speech recordings, and self-assessment questionnaires. PwPD were evaluated off and on optimal medication. Inferential analyses included Disease (controls vs. PwPD) and Language (French vs. Portuguese) as factors, and random decision forest algorithms identified relevant acoustic variables able to distinguish participants: (a) by language (French vs. Portuguese) and (b) by clinical status (PwPD on and off medication vs. controls). RESULTS French-speaking and Portuguese-speaking individuals were distinguished from each other with over 90% accuracy by five acoustic variables (the mean fundamental frequency and the shimmer of the sustained vowel /a/ production, the oral diadochokinesis performance index, the relative sound level pressure and the relative sound pressure level standard deviation of the text reading). A distinct set of parameters discriminated between controls and PwPD: for men, maximum phonation time and the oral diadochokinesis speech proportion were the most significant variables; for women, variables calculated from the oral diadochokinesis were the most discriminative. CONCLUSIONS Acoustic variables related to phonation and voice quality distinguished between speakers of the two languages. Variables related to pneumophonic coordination and articulation rate were the more effective in distinguishing PwPD from controls. Thus, our research findings support that respiration and diadochokinesis tasks appear to be the most appropriate to pinpoint signs of dysarthria, which are largely homogeneous and language-universal. In contrast, identifying language-specific variables with the speech tasks and acoustic variables studied was less conclusive.
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Affiliation(s)
- Serge Pinto
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
| | - Rita Cardoso
- CNS - Campus Neurológico Sénior, Torres Vedras, Portugal
- Instituto de Medicina Molecular, Faculdade de Medicina, University of Lisbon, Portugal
| | - Cyril Atkinson-Clement
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
- Precision Imaging Beacon, School of Medicine, University of Nottingham, United Kingdom
| | - Isabel Guimarães
- Instituto de Medicina Molecular, Faculdade de Medicina, University of Lisbon, Portugal
- Speech Therapy Department, Alcoitão Health School of Sciences, Alcabideche, Portugal
| | - Jasmin Sadat
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
| | - Helena Santos
- CNS - Campus Neurológico Sénior, Torres Vedras, Portugal
| | - Céline Mercier
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
- Neurology Department, Centre Hospitalier Intercommunal du Pays d'Aix, Aix-en-Provence, France
| | - Joana Carvalho
- CNS - Campus Neurológico Sénior, Torres Vedras, Portugal
| | | | | | - Pauline Welby
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
| | - Sónia Frota
- Center of Linguistics, School of Arts and Humanities, University of Lisbon, Portugal
| | | | - Marina Vigário
- Center of Linguistics, School of Arts and Humanities, University of Lisbon, Portugal
| | - Alban Letanneux
- ESPE Université Paris-Est Créteil, Laboratoire CHArt-UPEC (EA 4004), Bonneuil-sur-Marne, France
| | - Marisa Cruz
- Center of Linguistics, School of Arts and Humanities, University of Lisbon, Portugal
| | | | | | - Isabel Pavão Martins
- Language Research Laboratory, Department of Neurology, University of Lisbon, Portugal
| | - Rui Rothe-Neves
- Laboratório de Fonética, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - François Viallet
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
- Neurology Department, Centre Hospitalier Intercommunal du Pays d'Aix, Aix-en-Provence, France
| | - Joaquim J Ferreira
- CNS - Campus Neurológico Sénior, Torres Vedras, Portugal
- Instituto de Medicina Molecular, Faculdade de Medicina, University of Lisbon, Portugal
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Bhidayasiri R, Sringean J, Phumphid S, Anan C, Thanawattano C, Deoisres S, Panyakaew P, Phokaewvarangkul O, Maytharakcheep S, Buranasrikul V, Prasertpan T, Khontong R, Jagota P, Chaisongkram A, Jankate W, Meesri J, Chantadunga A, Rattanajun P, Sutaphan P, Jitpugdee W, Chokpatcharavate M, Avihingsanon Y, Sittipunt C, Sittitrai W, Boonrach G, Phonsrithong A, Suvanprakorn P, Vichitcholchai J, Bunnag T. The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand. Front Neurol 2024; 15:1386608. [PMID: 38803644 PMCID: PMC11129688 DOI: 10.3389/fneur.2024.1386608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/29/2024] Open
Abstract
The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Saisamorn Phumphid
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Chanawat Anan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | | | - Suwijak Deoisres
- National Electronics and Computer Technology Centre, Pathum Thani, Thailand
| | - Pattamon Panyakaew
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Onanong Phokaewvarangkul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Suppata Maytharakcheep
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Vijittra Buranasrikul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Tittaya Prasertpan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Sawanpracharak Hospital, Nakhon Sawan, Thailand
| | | | - Priya Jagota
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chaisongkram
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Worawit Jankate
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Jeeranun Meesri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chantadunga
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Piyaporn Rattanajun
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Phantakarn Sutaphan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Weerachai Jitpugdee
- Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Marisa Chokpatcharavate
- Chulalongkorn Parkinson's Disease Support Group, Department of Medicine, Faculty of Medicine, Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Yingyos Avihingsanon
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | - Chanchai Sittipunt
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | | | | | | | | | | | - Tej Bunnag
- Thai Red Cross Society, Bangkok, Thailand
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Ong YQ, Lee J, Chu SY, Chai SC, Gan KB, Ibrahim NM, Barlow SM. Oral-diadochokinesis between Parkinson's disease and neurotypical elderly among Malaysian-Malay speakers. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024. [PMID: 38451114 DOI: 10.1111/1460-6984.13025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/09/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND Parkinson's disease (PD) has an impact on speech production, manifesting in various ways including alterations in voice quality, challenges in articulating sounds and a decrease in speech rate. Numerous investigations have been conducted to ascertain the oral-diadochokinesis (O-DDK) rate in individuals with PD. However, the existing literature lacks exploration of such O-DDK rates in Malaysia and does not provide consistent evidence regarding the advantage of real-word repetition. AIMS To explore the effect of gender, stimuli type and PD status and their interactions on the O-DDK rates among Malaysian-Malay speakers. METHODS & PROCEDURES O-DDK performance of 62 participants (29 individuals with PD and 33 healthy elderly) using a non-word ('pataka'), a Malay real-word ('patahkan') and an English real-word ('buttercake') was audio recorded. The number of syllables produced in 8 s was counted. A hierarchical linear modelling was performed to investigate the effects of stimuli type (non-word, Malay real-word, English real-word), PD status (yes, no), gender (male, female) and their interactions on the O-DDK rate. The model accounted for participants' age as well as the nesting of repeated measurements within participants, thereby providing unbiased estimates of the effects. OUTCOMES & RESULTS The stimuli effect was significant (p < 0.0001). Malay real-word showed the lowest O-DDK rate (5.03 ± 0.11 syllables/s), followed by English real-word (5.25 ± 0.11 syllables/s) and non-word (5.42 ± 0.11 syllables/s). Individuals with PD showed a significantly lower O-DDK rate compared to healthy elderly (4.73 ± 0.15 syllables/s vs. 5.74 ± 0.14 syllables/s, adjusted p < 0.001). A subsequent analysis indicated that the O-DDK rate declined in a quadratic pattern. However, neither gender nor age effects were observed. Additionally, no significant two-way interactions were found between stimuli type, PD status and gender (all p > 0.05). Therefore, the choice of stimuli type has no or only limited effect considering the use of O-DDK tests in clinical practice for diagnostic purposes. CONCLUSIONS & IMPLICATIONS The observed slowness in O-DDK among individuals with PD can be attributed to the impact of the movement disorder, specifically bradykinesia, on the physiological aspects of speech production. Speech-language pathologists can gain insights into the impact of PD on speech production and tailor appropriate intervention strategies to address the specific needs of individuals with PD according to disease stages. WHAT THIS PAPER ADDS What is already known on this subject The observed slowness in O-DDK rates among individuals with PD may stem from the movement disorder's effects on the physiological aspects of speech production, particularly bradykinesia. However, there is a lack of consistent evidence regarding the influence of real-word repetition and how O-DDK rates vary across different PD stages. What this study adds to existing knowledge The O-DDK rates decline in a quadratic pattern as the PD progresses. The research provides insights into the advantage of real-word repetition in assessing O-DDK rates, with Malay real-word showing the lowest O-DDK rate, followed by English real-word and non-word. What are the potential or actual clinical implications of this work? Speech-language pathologists can better understand the evolving nature of speech motor impairments as PD progresses. This insight enables them to design targeted intervention strategies that are sensitive to the specific needs and challenges associated with each PD stage. This finding can guide clinicians in selecting appropriate assessment tools for evaluating speech motor function in PD patients.
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Affiliation(s)
- Ying Qian Ong
- Centre for Healthy Ageing and Wellness (H-CARE), Faculty of Health Sciences, Speech Sciences Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Jaehoon Lee
- Department of Educational Psychology, Leadership, and Counseling, Texas Tech University, Lubbock, Texas, USA
| | - Shin Ying Chu
- Centre for Healthy Ageing and Wellness (H-CARE), Faculty of Health Sciences, Speech Sciences Programme, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Siaw Chui Chai
- Centre for Rehabilitation & Special Needs Studies, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Kok Beng Gan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Norlinah Mohamed Ibrahim
- Department of Medicine, Hospital Canselor Tuanku Muhriz, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Steven M Barlow
- Special Education & Communication Disorders, Biomedical Engineering, Center for Brain, Biology, Behavior, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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Šubert M, Novotný M, Tykalová T, Hlavnička J, Dušek P, Růžička E, Škrabal D, Pelletier A, Postuma RB, Montplaisir J, Gagnon JF, Galbiati A, Ferini-Strambi L, Marelli S, St Louis EK, Timm PC, Teigen LN, Janzen A, Oertel W, Heim B, Holzknecht E, Stefani A, Högl B, Dauvilliers Y, Evangelista E, Šonka K, Rusz J. Spoken Language Alterations can Predict Phenoconversion in Isolated Rapid Eye Movement Sleep Behavior Disorder: A Multicenter Study. Ann Neurol 2024; 95:530-543. [PMID: 37997483 DOI: 10.1002/ana.26835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVE This study assessed the relationship between speech and language impairment and outcome in a multicenter cohort of isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). METHODS Patients with iRBD from 7 centers speaking Czech, English, German, French, and Italian languages underwent a detailed speech assessment at baseline. Story-tale narratives were transcribed and linguistically annotated using fully automated methods based on automatic speech recognition and natural language processing algorithms, leading to the 3 distinctive linguistic and 2 acoustic patterns of language deterioration and associated composite indexes of their overall severity. Patients were then prospectively followed and received assessments for parkinsonism or dementia during follow-up. The Cox proportional hazard was performed to evaluate the predictive value of language patterns for phenoconversion over a follow-up period of 5 years. RESULTS Of 180 patients free of parkinsonism or dementia, 156 provided follow-up information. After a mean follow-up of 2.7 years, 42 (26.9%) patients developed neurodegenerative disease. Patients with higher severity of linguistic abnormalities (hazard ratio [HR = 2.35]) and acoustic abnormalities (HR = 1.92) were more likely to develop a defined neurodegenerative disease, with converters having lower content richness (HR = 1.74), slower articulation rate (HR = 1.58), and prolonged pauses (HR = 1.46). Dementia-first (n = 16) and parkinsonism-first with mild cognitive impairment (n = 9) converters had higher severity of linguistic abnormalities than parkinsonism-first with normal cognition converters (n = 17). INTERPRETATION Automated language analysis might provide a predictor of phenoconversion from iRBD into synucleinopathy subtypes with cognitive impairment, and thus can be used to stratify patients for neuroprotective trials. ANN NEUROL 2024;95:530-543.
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Affiliation(s)
- Martin Šubert
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Hlavnička
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Evžen Růžička
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Dominik Škrabal
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Amelie Pelletier
- Department of Neurology, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Ronald B Postuma
- Department of Neurology, Research Institute of the McGill University Health Centre, Montreal General Hospital, Montreal, Quebec, Canada
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Jacques Montplaisir
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Jean-François Gagnon
- Center for Advanced Research in Sleep Medicine, CIUSSS-NÎM - Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Andrea Galbiati
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
- Department of Psychology, "Vita-Salute" San Raffaele University, Milan, Italy
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
- Department of Psychology, "Vita-Salute" San Raffaele University, Milan, Italy
| | - Sara Marelli
- Sleep Disorders Center, Division of Neuroscience, Ospedale San Raffaele, Università Vita-Salute, Milan, Italy
| | - Erik K St Louis
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
- Mayo Clinic Health System Southwest Wisconsin, La Crosse, WI, USA
| | - Paul C Timm
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
| | - Luke N Teigen
- Mayo Center for Sleep Medicine, and Sleep Behavior and Neurophysiology Research Laboratory, Departments of Neurology and Medicine, Division of Pulmonary and Critical Care Medicine Mayo Clinic College of Medicine and Science Rochester, Rochester, MN, USA
| | - Annette Janzen
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Wolfgang Oertel
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Beatrice Heim
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Evi Holzknecht
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Ambra Stefani
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Yves Dauvilliers
- National Reference Network for Narcolepsy, Sleep-Wake Disorder Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, INSERM, University of Montpellier, Montpellier, France
| | - Elisa Evangelista
- National Reference Network for Narcolepsy, Sleep-Wake Disorder Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, INSERM, University of Montpellier, Montpellier, France
| | - Karel Šonka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
- Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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Rios-Urrego CD, Rusz J, Orozco-Arroyave JR. Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach. NPJ Digit Med 2024; 7:37. [PMID: 38368458 PMCID: PMC10874421 DOI: 10.1038/s41746-024-01027-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024] Open
Abstract
Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.
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Affiliation(s)
| | - Jan Rusz
- Department of Circuit Theory, Czech Technical University in Prague, Prague, Czech Republic.
| | - Juan Rafael Orozco-Arroyave
- GITA Lab, Faculty of Engineering, University of Antioquia, Medellín, Colombia.
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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Benítez-Burraco A, Ivanova O. Language in healthy and pathological ageing: Methodological milestones and challenges. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:4-12. [PMID: 38149881 DOI: 10.1111/1460-6984.13003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Affiliation(s)
- Antonio Benítez-Burraco
- Department of Spanish, Linguistics and Theory of Literature (Linguistics), Faculty of Philology, University of Seville, Sevilla, Spain
| | - Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca/Institute of Neuroscience of Castilla y León (INCYL), Salamanca, Spain
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Ivanova O, Martínez-Nicolás I, Meilán JJG. Speech changes in old age: Methodological considerations for speech-based discrimination of healthy ageing and Alzheimer's disease. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2024; 59:13-37. [PMID: 37140204 DOI: 10.1111/1460-6984.12888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/03/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Recent evidence suggests that speech substantially changes in ageing. As a complex neurophysiological process, it can accurately reflect changes in the motor and cognitive systems underpinning human speech. Since healthy ageing is not always easily discriminable from early stages of dementia based on cognitive and behavioural hallmarks, speech is explored as a preclinical biomarker of pathological itineraries in old age. A greater and more specific impairment of neuromuscular activation, as well as a specific cognitive and linguistic impairment in dementia, unchain discriminating changes in speech. Yet, there is no consensus on such discriminatory speech parameters, neither on how they should be elicited and assessed. AIMS To provide a state-of-the-art on speech parameters that allow for early discrimination between healthy and pathological ageing; the aetiology of these parameters; the effect of the type of experimental stimuli on speech elicitation and the predictive power of different speech parameters; and the most promising methods for speech analysis and their clinical implications. METHODS & PROCEDURES A scoping review methodology is used in accordance with the PRISMA model. Following a systematic search of PubMed, PsycINFO and CINAHL, 24 studies are included and analysed in the review. MAIN CONTRIBUTION The results of this review yield three key questions for the clinical assessment of speech in ageing. First, acoustic and temporal parameters are more sensitive to changes in pathological ageing and, of these two, temporal variables are more affected by cognitive impairment. Second, different types of stimuli can trigger speech parameters with different degree of accuracy for the discrimination of clinical groups. Tasks with higher cognitive load are more precise in eliciting higher levels of accuracy. Finally, automatic speech analysis for the discrimination of healthy and pathological ageing should be improved for both research and clinical practice. CONCLUSIONS & IMPLICATIONS Speech analysis is a promising non-invasive tool for the preclinical screening of healthy and pathological ageing. The main current challenges of speech analysis in ageing are the automatization of its clinical assessment and the consideration of the speaker's cognitive background during evaluation. WHAT THIS PAPER ADDS What is already known on the subject Societal aging goes hand in hand with the rising incidence of ageing-related neurodegenerations, mainly Alzheimer's disease (AD). This is particularly noteworthy in countries with longer life expectancies. Healthy ageing and early stages of AD share a set of cognitive and behavioural characteristics. Since there is no cure for dementias, developing methods for accurate discrimination of healthy ageing and early AD is currently a priority. Speech has been described as one of the most significantly impaired features in AD. Neuropathological alterations in motor and cognitive systems would underlie specific speech impairment in dementia. Since speech can be evaluated quickly, non-invasively and inexpensively, its value for the clinical assessment of ageing itineraries may be particularly high. What this paper adds to existing knowledge Theoretical and experimental advances in the assessment of speech as a marker of AD have developed rapidly over the last decade. Yet, they are not always known to clinicians. Furthermore, there is a need to provide an updated state-of-the-art on which speech features are discriminatory to AD, how they can be assessed, what kind of results they can yield, and how such results should be interpreted. This article provides an updated overview of speech profiling, methods of speech measurement and analysis, and the clinical power of speech assessment for early discrimination of AD as the most common cause of dementia. What are the potential or actual clinical implications of this work? This article provides an overview of the predictive potential of different speech parameters in relation to AD cognitive impairment. In addition, it discusses the effect that the cognitive state, the type of elicitation task and the type of assessment method may have on the results of the speech-based analysis in ageing.
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Affiliation(s)
- Olga Ivanova
- Spanish Language Department, Faculty of Philology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Israel Martínez-Nicolás
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
| | - Juan José García Meilán
- Department of Basic Psychology, Psychobiology and Behavioral Science Methodology, Faculty of Psychology, University of Salamanca, Salamanca, Spain
- Institute of Neuroscience of Castilla y León, Salamanca, Spain
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Iyer A, Kemp A, Rahmatallah Y, Pillai L, Glover A, Prior F, Larson-Prior L, Virmani T. A machine learning method to process voice samples for identification of Parkinson's disease. Sci Rep 2023; 13:20615. [PMID: 37996478 PMCID: PMC10667335 DOI: 10.1038/s41598-023-47568-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023] Open
Abstract
Machine learning approaches have been used for the automatic detection of Parkinson's disease with voice recordings being the most used data type due to the simple and non-invasive nature of acquiring such data. Although voice recordings captured via telephone or mobile devices allow much easier and wider access for data collection, current conflicting performance results limit their clinical applicability. This study has two novel contributions. First, we show the reliability of personal telephone-collected voice recordings of the sustained vowel /a/ in natural settings by collecting samples from 50 people with specialist-diagnosed Parkinson's disease and 50 healthy controls and applying machine learning classification with voice features related to phonation. Second, we utilize a novel application of a pre-trained convolutional neural network (Inception V3) with transfer learning to analyze the spectrograms of the sustained vowel from these samples. This approach considers speech intensity estimates across time and frequency scales rather than collapsing measurements across time. We show the superiority of our deep learning model for the task of classifying people with Parkinson's disease as distinct from healthy controls.
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Affiliation(s)
- Anu Iyer
- Georgia Institute of Technology, Atlanta, 30332, USA
| | - Aaron Kemp
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA.
| | - Yasir Rahmatallah
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Lakshmi Pillai
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Aliyah Glover
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Fred Prior
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Linda Larson-Prior
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurobiology and Developmental Sciences, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
| | - Tuhin Virmani
- Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
- Neurology, University of Arkansas for Medical Sciences, Little Rock, 72205, USA
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10
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Suppa A, Asci F, Costantini G, Bove F, Piano C, Pistoia F, Cerroni R, Brusa L, Cesarini V, Pietracupa S, Modugno N, Zampogna A, Sucapane P, Pierantozzi M, Tufo T, Pisani A, Peppe A, Stefani A, Calabresi P, Bentivoglio AR, Saggio G. Effects of deep brain stimulation of the subthalamic nucleus on patients with Parkinson's disease: a machine-learning voice analysis. Front Neurol 2023; 14:1267360. [PMID: 37928137 PMCID: PMC10622670 DOI: 10.3389/fneur.2023.1267360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/20/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Deep brain stimulation of the subthalamic nucleus (STN-DBS) can exert relevant effects on the voice of patients with Parkinson's disease (PD). In this study, we used artificial intelligence to objectively analyze the voices of PD patients with STN-DBS. Materials and methods In a cross-sectional study, we enrolled 108 controls and 101 patients with PD. The cohort of PD was divided into two groups: the first group included 50 patients with STN-DBS, and the second group included 51 patients receiving the best medical treatment. The voices were clinically evaluated using the Unified Parkinson's Disease Rating Scale part-III subitem for voice (UPDRS-III-v). We recorded and then analyzed voices using specific machine-learning algorithms. The likelihood ratio (LR) was also calculated as an objective measure for clinical-instrumental correlations. Results Clinically, voice impairment was greater in STN-DBS patients than in those who received oral treatment. Using machine learning, we objectively and accurately distinguished between the voices of STN-DBS patients and those under oral treatments. We also found significant clinical-instrumental correlations since the greater the LRs, the higher the UPDRS-III-v scores. Discussion STN-DBS deteriorates speech in patients with PD, as objectively demonstrated by machine-learning voice analysis.
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Affiliation(s)
- Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, IS, Italy
| | - Francesco Asci
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, IS, Italy
| | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Carla Piano
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Francesca Pistoia
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Coppito, AQ, Italy
- Neurology Unit, San Salvatore Hospital, Coppito, AQ, Italy
| | - Rocco Cerroni
- Department of System Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Livia Brusa
- Neurology Unit, S. Eugenio Hospital, Rome, Italy
| | - Valerio Cesarini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Sara Pietracupa
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, IS, Italy
| | | | | | | | | | - Tommaso Tufo
- Neurosurgery Unit, Policlinico A. Gemelli University Hospital Foundation IRCSS, Rome, Italy
- Neurosurgery Department, Fakeeh University Hospital, Dubai, United Arab Emirates
| | - Antonio Pisani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | | | - Alessandro Stefani
- Department of System Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Paolo Calabresi
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
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11
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Johansson IL, Samuelsson C, Müller N. Consonant articulation acoustics and intelligibility in Swedish speakers with Parkinson's disease: a pilot study. CLINICAL LINGUISTICS & PHONETICS 2023; 37:845-865. [PMID: 35833475 DOI: 10.1080/02699206.2022.2095926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 05/16/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Imprecise consonant articulation is common in speakers with Parkinson's disease and can affect intelligibility. The research on the relationship between acoustic speech measures and intelligibility in Parkinson's disease is limited, and most of the research has been conducted on English. This pilot study investigated aspects of consonant articulation acoustics in eleven Swedish speakers with Parkinson's disease and six neurologically healthy persons. The focus of the study was on consonant cluster production, articulatory motion rate and variation, and voice onset time, and how these acoustic features correlate with speech intelligibility. Among the measures in the present study, typicality ratings of heterorganic consonant clusters /spr/ and /skr/ had the strongest correlations with intelligibility. Measures based on syllable repetition, such as repetition rate and voice onset time, showed varying results with weak to moderate correlations with intelligibility. One conclusion is that some acoustic measures may be more sensitive than others to the impact of the underlying sensory-motor impairment and dysarthria on speech production and intelligibility in speakers with Parkinson's disease. Some aspects of articulation appear to be equally demanding in terms of acoustic realisation for elderly healthy speakers and for speakers with Parkinson's disease, such as sequential motion rate measures. Clinically, this would imply that for the purpose of detecting signs of disordered speech motor control, choosing measures with less variation among older speakers without articulation impairment would lead to more robust results.
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Affiliation(s)
- Inga-Lena Johansson
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
| | - Christina Samuelsson
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
- Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute, Solna, Sweden
| | - Nicole Müller
- Department of Biomedical and Clinical Sciences/Speech and Language Pathology, Linköping University, Linköping, Sweden
- Department of Speech and Hearing Sciences, University College Cork, Cork, Ireland
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12
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Roland V, Huet K, Harmegnies B, Piccaluga M, Verhaegen C, Delvaux V. Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson's disease. Front Psychol 2023; 14:1129830. [PMID: 37701868 PMCID: PMC10493417 DOI: 10.3389/fpsyg.2023.1129830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/26/2023] [Indexed: 09/14/2023] Open
Abstract
Objectives Our aim is to detect early, subclinical speech biomarkers of dysarthria in Parkinson's disease (PD), i.e., systematic atypicalities in speech that remain subtle, are not easily detectible by the clinician, so that the patient is labeled "non-dysarthric." Based on promising exploratory work, we examine here whether vowel articulation, as assessed by three acoustic metrics, can be used as early indicator of speech difficulties associated with Parkinson's disease. Study design This is a prospective case-control study. Methods Sixty-three individuals with PD and 35 without PD (healthy controls-HC) participated in this study. Out of 63 PD patients, 43 had been diagnosed with dysarthria (DPD) and 20 had not (NDPD). Sustained vowels were recorded for each speaker and formant frequencies were measured. The analyses focus on three acoustic metrics: individual vowel triangle areas (tVSA), vowel articulation index (VAI) and the Phi index. Results tVSA were found to be significantly smaller for DPD speakers than for HC. The VAI showed significant differences between these two groups, indicating greater centralization and lower vowel contrasts in the DPD speakers with dysarhtria. In addition, DPD and NDPD speakers had lower Phi values, indicating a lower organization of their vowel system compared to the HC. Results also showed that the VAI index was the most efficient to distinguish between DPD and NDPD whereas the Phi index was the best acoustic metric to discriminate NDPD and HC. Conclusion This acoustic study identified potential subclinical vowel-related speech biomarkers of dysarthria in speakers with Parkinson's disease who have not been diagnosed with dysarthria.
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Affiliation(s)
- Virginie Roland
- Metrology and Language Sciences Unit, Mons, Belgium
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
| | - Kathy Huet
- Metrology and Language Sciences Unit, Mons, Belgium
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
| | - Bernard Harmegnies
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
| | - Myriam Piccaluga
- Metrology and Language Sciences Unit, Mons, Belgium
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
| | - Clémence Verhaegen
- Metrology and Language Sciences Unit, Mons, Belgium
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
| | - Véronique Delvaux
- Metrology and Language Sciences Unit, Mons, Belgium
- Research Institute for Language Science and Technology, University of Mons, Mons, Belgium
- National Fund for Scientific Research, Brussels, Belgium
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13
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Mondol SIMMR, Kim R, Lee S. Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels. Bioengineering (Basel) 2023; 10:984. [PMID: 37627869 PMCID: PMC10451837 DOI: 10.3390/bioengineering10080984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
Recent research has achieved a great classification rate for separating healthy people from those with Parkinson's disease (PD) using speech and the voice. However, these studies have primarily treated early and advanced stages of PD as equal entities, neglecting the distinctive speech impairments and other symptoms that vary across the different stages of the disease. To address this limitation, and improve diagnostic precision, this study assesses the selected acoustic features of dysphonia, as they relate to PD and the Hoehn and Yahr stages, by combining various preprocessing techniques and multiple classification algorithms, to create a comprehensive and robust solution for classification tasks. The dysphonia features extracted from the three sustained Korean vowels /아/(a), /이/(i), and /우/(u) exhibit diversity and strong correlations. To address this issue, the analysis of variance F-Value feature selection classifier from scikit-learn was employed, to identify the topmost relevant features. Additionally, to overcome the class imbalance problem, the synthetic minority over-sampling technique was utilized. To ensure fair comparisons, and mitigate the influence of individual classifiers, four commonly used machine learning classifiers, namely random forest (RF), support vector machine (SVM), k-nearest neighbor (kNN), and multi-layer perceptron (MLP), were employed. This approach enables a comprehensive evaluation of the feature extraction methods, and minimizes the variance in the final classification models. The proposed hybrid machine learning pipeline using the acoustic features of sustained vowels efficiently detects the early and mid-advanced stages of PD with a detection accuracy of 95.48%, and with a detection accuracy of 86.62% for the 4-stage, and a detection accuracy of 89.48% for the 3-stage classification of PD. This study successfully demonstrates the significance of utilizing the diverse acoustic features of dysphonia in the classification of PD and its stages.
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Affiliation(s)
- S. I. M. M. Raton Mondol
- Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Ryul Kim
- Department of Neurology, Inha University Hospital, Inha University College of Medicine, Incheon 22212, Republic of Korea
| | - Sangmin Lee
- Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea
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14
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Zhu X, Dai G, Wang M, Tan M, Li Y, Xu Z, Lei D, Chen L, Chen X, Liu H. Continuous theta burst stimulation over right cerebellum for speech impairment in Parkinson's disease: study protocol for a randomized, sham-controlled, clinical trial. Front Aging Neurosci 2023; 15:1215330. [PMID: 37655339 PMCID: PMC10465698 DOI: 10.3389/fnagi.2023.1215330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/03/2023] [Indexed: 09/02/2023] Open
Abstract
Background Speech impairment is a common symptom of Parkinson's disease (PD) that worsens with disease progression and affects communication and quality of life. Current pharmacological and surgical treatments for PD have inconsistent effects on speech impairment. The cerebellum is an essential part of sensorimotor network that regulates speech production and becomes dysfunctional in PD. Continuous theta-burst stimulation (cTBS) is a non-invasive brain stimulation technique that can modulate the cerebellum and its connections with other brain regions. Objective To investigate whether cTBS over the right cerebellum coupled with speech-language therapy (SLT) can improve speech impairment in PD. Methods In this randomized controlled trial (RCT), 40 patients with PD will be recruited and assigned to either an experimental group (EG) or a control group (CG). Both groups will receive 10 sessions of standard SLT. The EG will receive real cTBS over the right cerebellum, while the CG will receive sham stimulation. Blinded assessors will evaluate the treatment outcome at three time points: pre-intervention, post-intervention, and at a 12-week follow-up. The primary outcome measures are voice/speech quality and neurobehavioral parameters of auditory-vocal integration. The secondary outcome measures are cognitive function, quality of life, and functional connectivity determined by resting-state functional magnetic resonance imaging (fMRI). Significance This trial will provide evidence for the efficacy and safety of cerebellar cTBS for the treatment of speech impairment in PD and shed light on the neural mechanism of this intervention. It will also have implications for other speech impairment attributed to cerebellar dysfunctions. Clinical trial registration www.chictr.org.cn, identifier ChiCTR2100050543.
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Affiliation(s)
- Xiaoxia Zhu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guangyan Dai
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mingdan Tan
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongxue Li
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhiqin Xu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Di Lei
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ling Chen
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xi Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hanjun Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
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15
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Rowe HP, Shellikeri S, Yunusova Y, Chenausky KV, Green JR. Quantifying articulatory impairments in neurodegenerative motor diseases: A scoping review and meta-analysis of interpretable acoustic features. INTERNATIONAL JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2023; 25:486-499. [PMID: 36001500 PMCID: PMC9950294 DOI: 10.1080/17549507.2022.2089234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
PURPOSE Neurodegenerative motor diseases (NMDs) have devastating effects on the lives of patients and their loved ones, in part due to the impact of neurologic abnormalities on speech, which significantly limits functional communication. Clinical speech researchers have thus spent decades investigating speech features in populations suffering from NMDs. Features of impaired articulatory function are of particular interest given their detrimental impact on intelligibility, their ability to encode a variety of distinct movement disorders, and their potential as diagnostic indicators of neurodegenerative diseases. The objectives of this scoping review were to identify (1) which components of articulation (i.e. coordination, consistency, speed, precision, and repetition rate) are the most represented in the acoustic literature on NMDs; (2) which acoustic articulatory features demonstrate the most potential for detecting speech motor dysfunction in NMDs; and (3) which articulatory components are the most impaired within each NMD. METHOD This review examined literature published between 1976 and 2020. Studies were identified from six electronic databases using predefined key search terms. The first research objective was addressed using a frequency count of studies investigating each articulatory component, while the second and third objectives were addressed using meta-analyses. RESULT Findings from 126 studies revealed a considerable emphasis on articulatory precision. Of the 24 features included in the meta-analyses, vowel dispersion/distance and stop gap duration exhibited the largest effects when comparing the NMD population to controls. The meta-analyses also revealed divergent patterns of articulatory performance across disease types, providing evidence of unique profiles of articulatory impairment. CONCLUSION This review illustrates the current state of the literature on acoustic articulatory features in NMDs. By highlighting the areas of need within each articulatory component and disease group, this work provides a foundation on which clinical researchers, speech scientists, neurologists, and computer science engineers can develop research questions that will both broaden and deepen the understanding of articulatory impairments in NMDs.
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Affiliation(s)
- Hannah P Rowe
- MGH Institute of Health Professions, Boston, MA, USA
| | - Sanjana Shellikeri
- Department of Speech-Language Pathology & Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Yana Yunusova
- Department of Speech-Language Pathology & Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Toronto, ON, Canada
| | - Karen V Chenausky
- MGH Institute of Health Professions, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA, and
| | - Jordan R Green
- MGH Institute of Health Professions, Boston, MA, USA
- Speech and Hearing Biosciences and Technology Program, Harvard University, Cambridge, MA, USA
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16
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Illner V, Tykalova T, Skrabal D, Klempir J, Rusz J. Automated Vowel Articulation Analysis in Connected Speech Among Progressive Neurological Diseases, Dysarthria Types, and Dysarthria Severities. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023:1-22. [PMID: 37499137 DOI: 10.1044/2023_jslhr-22-00526] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
PURPOSE Although articulatory impairment represents distinct speech characteristics in most neurological diseases affecting movement, methods allowing automated assessments of articulation deficits from the connected speech are scarce. This study aimed to design a fully automated method for analyzing dysarthria-related vowel articulation impairment and estimate its sensitivity in a broad range of neurological diseases and various types and severities of dysarthria. METHOD Unconstrained monologue and reading passages were acquired from 459 speakers, including 306 healthy controls and 153 neurological patients. The algorithm utilized a formant tracker in combination with a phoneme recognizer and subsequent signal processing analysis. RESULTS Articulatory undershoot of vowels was presented in a broad spectrum of progressive neurodegenerative diseases, including Parkinson's disease, progressive supranuclear palsy, multiple-system atrophy, Huntington's disease, essential tremor, cerebellar ataxia, multiple sclerosis, and amyotrophic lateral sclerosis, as well as in related dysarthria subtypes including hypokinetic, hyperkinetic, ataxic, spastic, flaccid, and their mixed variants. Formant ratios showed a higher sensitivity to vowel deficits than vowel space area. First formants of corner vowels were significantly lower for multiple-system atrophy than cerebellar ataxia. Second formants of vowels /a/ and /i/ were lower in ataxic compared to spastic dysarthria. Discriminant analysis showed a classification score of up to 41.0% for disease type, 39.3% for dysarthria type, and 49.2% for dysarthria severity. Algorithm accuracy reached an F-score of 0.77. CONCLUSIONS Distinctive vowel articulation alterations reflect underlying pathophysiology in neurological diseases. Objective acoustic analysis of vowel articulation has the potential to provide a universal method to screen motor speech disorders. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.23681529.
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Affiliation(s)
- Vojtech Illner
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Tereza Tykalova
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Dominik Skrabal
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jiri Klempir
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
- Department of Neurology and ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Switzerland
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Hemmerling D, Wodzinski M, Orozco-Arroyave JR, Sztaho D, Daniol M, Jemiolo P, Wojcik-Pedziwiatr M. Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083719 DOI: 10.1109/embc40787.2023.10340478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Parkinson's disease (PD) is the 2nd most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson's disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance- There is an urgent need to develop non invasive biomarker of Parkinson's disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases' symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.
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Scimeca S, Amato F, Olmo G, Asci F, Suppa A, Costantini G, Saggio G. Robust and language-independent acoustic features in Parkinson's disease. Front Neurol 2023; 14:1198058. [PMID: 37384279 PMCID: PMC10294689 DOI: 10.3389/fneur.2023.1198058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/26/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction The analysis of vocal samples from patients with Parkinson's disease (PDP) can be relevant in supporting early diagnosis and disease monitoring. Intriguingly, speech analysis embeds several complexities influenced by speaker characteristics (e.g., gender and language) and recording conditions (e.g., professional microphones or smartphones, supervised, or non-supervised data collection). Moreover, the set of vocal tasks performed, such as sustained phonation, reading text, or monologue, strongly affects the speech dimension investigated, the feature extracted, and, as a consequence, the performance of the overall algorithm. Methods We employed six datasets, including a cohort of 176 Healthy Control (HC) participants and 178 PDP from different nationalities (i.e., Italian, Spanish, Czech), recorded in variable scenarios through various devices (i.e., professional microphones and smartphones), and performing several speech exercises (i.e., vowel phonation, sentence repetition). Aiming to identify the effectiveness of different vocal tasks and the trustworthiness of features independent of external co-factors such as language, gender, and data collection modality, we performed several intra- and inter-corpora statistical analyses. In addition, we compared the performance of different feature selection and classification models to evaluate the most robust and performing pipeline. Results According to our results, the combined use of sustained phonation and sentence repetition should be preferred over a single exercise. As for the set of features, the Mel Frequency Cepstral Coefficients demonstrated to be among the most effective parameters in discriminating between HC and PDP, also in the presence of heterogeneous languages and acquisition techniques. Conclusion Even though preliminary, the results of this work can be exploited to define a speech protocol that can effectively capture vocal alterations while minimizing the effort required to the patient. Moreover, the statistical analysis identified a set of features minimally dependent on gender, language, and recording modalities. This discloses the feasibility of extensive cross-corpora tests to develop robust and reliable tools for disease monitoring and staging and PDP follow-up.
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Affiliation(s)
- Sabrina Scimeca
- Department of Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy
| | - Federica Amato
- Department of Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy
| | - Gabriella Olmo
- Department of Control and Computer Engineering, Polytechnic University of Turin, Turin, Italy
| | - Francesco Asci
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
| | - Antonio Suppa
- Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed Institute, Pozzilli, Italy
| | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
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Thies T, Mücke D, Geerts N, Seger A, Fink GR, Barbe MT, Sommerauer M. Compensatory articulatory mechanisms preserve intelligibility in prodromal Parkinson's disease. Parkinsonism Relat Disord 2023; 112:105487. [PMID: 37329726 DOI: 10.1016/j.parkreldis.2023.105487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/19/2023]
Abstract
INTRODUCTION Dysarthria is highly prevalent in patients with Parkinson's disease (PD) and speech changes have already been detected in patients with prodromal PD on the acoustic level. However, the present study directly tracks underlying articulatory movements with electromagnetic articulography to investigate early speech alterations on the kinematic level in isolated REM sleep behavior disorder (iRBD) and compares them to PD and control speakers. METHODS Kinematic data of 23 control speakers, 22 speakers with iRBD, and 23 speakers with PD were collected. Amplitude, duration, and average speed of lower lip, tongue tip, and tongue body movements were analyzed. Naive listeners rated the intelligibility of all speakers. RESULTS Patients with iRBD produced tongue tip and tongue body movements that were larger in amplitude and longer in duration compared to control speakers, while remaining intelligible. Compared to patients with iRBD, patients with PD had smaller, longer and slower tongue tip and lower lip movements, accompanied by lower intelligibility. Thus, the data indicate that the lingual system is already affected in prodromal PD. Furthermore, lower lip and especially tongue tip movements slow down and speech intelligibility decreases if motor impairment is more pronounced. CONCLUSION Patients with iRBD adjust articulatory patterns to counteract incipient motor detriment on speech to maintain their intelligibility level.
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Affiliation(s)
- Tabea Thies
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; University of Cologne, Faculty of Arts and Humanities, IfL Phonetics, Germany.
| | - Doris Mücke
- University of Cologne, Faculty of Arts and Humanities, IfL Phonetics, Germany
| | - Nuria Geerts
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany
| | - Aline Seger
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Germany
| | - Gereon R Fink
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Germany
| | - Michael T Barbe
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany
| | - Michael Sommerauer
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Neurology, Germany; Institute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Germany
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20
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Biswas SK, Nath Boruah A, Saha R, Raj RS, Chakraborty M, Bordoloi M. Early detection of Parkinson disease using stacking ensemble method. Comput Methods Biomech Biomed Engin 2023; 26:527-539. [PMID: 35587795 DOI: 10.1080/10255842.2022.2072683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Parkinson's disease (PD) is a common progressive neurodegenerative disorder that occurs due to corrosion of the substantianigra, located in the thalamic region of the human brain, and is responsible for the transmission of neural signals throughout the human body using brain chemical, termed as "dopamine." Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include the presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Hence, sometimes the process of diagnosis may also be affected by human error. To overcome this problem some intelligent models have been proposed; however, most of them are single classifier-based models and due to this these models cannot handle noisy and imbalanced data properly and thus sometimes overfit the model. To reduce bias and variance, and to avoid overfitting of a single classifier-based model, this paper proposes an ensemble-based PD diagnosis model, named Ensembled Expert System for Diagnosis of Parkinson's Disease (EESDPD) with relevant features and a simple stacking ensemble technique. The proposed EESDPD aggregates diverse assumptions for making the prediction. The performance of the proposed EESDPD is compared with the performances of logistic regression, SVM, Naïve Bayes, Random Forest, XGBoost, simple Decision Tree, B-TDS-PD and B-TESM-PD in terms of classification accuracy, precision, recall and F1-score measures.
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Affiliation(s)
- Saroj Kumar Biswas
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Arpita Nath Boruah
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Rajib Saha
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Ravi Shankar Raj
- Computer Science and Engineering Department, National Institute of Technology, Silchar, India
| | - Manomita Chakraborty
- School of Computer Science and Engineering, VIT-AP University, Amaravathi, India
| | - Monali Bordoloi
- School of Computer Science and Engineering, VIT-AP University, Amaravathi, India
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21
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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Rusz J, Krupička R, Vítečková S, Tykalová T, Novotný M, Novák J, Dušek P, Růžička E. Speech and gait abnormalities in motor subtypes of de-novo Parkinson's disease. CNS Neurosci Ther 2023. [PMID: 36942517 DOI: 10.1111/cns.14158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/22/2023] [Accepted: 02/24/2023] [Indexed: 03/23/2023] Open
Abstract
AIM To investigate the presence and relationship of temporal speech and gait parameters in patients with postural instability/gait disorder (PIGD) and tremor-dominant (TD) motor subtypes of Parkinson's disease (PD). METHODS Speech samples and instrumented walkway system assessments were acquired from a total of 60 de-novo PD patients (40 in TD and 20 in PIGD subtype) and 40 matched healthy controls. Objective acoustic vocal assessment of seven distinct speech timing dimensions was related to instrumental gait measures including velocity, cadence, and stride length. RESULTS Compared to controls, PIGD subtype showed greater consonant timing abnormalities by prolonged voice onset time (VOT) while also shorter stride length during both normal walking and dual task, while decreased velocity and cadence only during dual task. Speaking rate was faster in PIGD than TD subtype. In PIGD subtype, prolonged VOT correlated with slower gait velocity (r = -0.56, p = 0.01) and shorter stride length (r = -0.59, p = 0.008) during normal walking, whereas relationships were also found between decreased cadence in dual task and irregular alternating motion rates (r = -0.48, p = 0.04) and prolonged pauses (r = -0.50, p = 0.03). No correlation between speech and gait was detected in TD subtype. CONCLUSION Our findings suggest that speech and gait rhythm disorder share similar underlying pathomechanisms specific for PIGD subtype.
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Affiliation(s)
- Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
- Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Radim Krupička
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Slávka Vítečková
- Faculty of Biomedical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
| | - Jan Novák
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
- Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Prague, Czechia
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
- Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
| | - Evžen Růžička
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
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23
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Novotny M, Cmejla R, Tykalova T. Automated prediction of children's age from voice acoustics. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Sharma VK, Singh TG, Mehta V, Mannan A. Biomarkers: Role and Scope in Neurological Disorders. Neurochem Res 2023; 48:2029-2058. [PMID: 36795184 DOI: 10.1007/s11064-023-03873-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/19/2023] [Accepted: 01/21/2023] [Indexed: 02/17/2023]
Abstract
Neurological disorders pose a great threat to social health and are a major cause for mortality and morbidity. Effective drug development complemented with the improved drug therapy has made considerable progress towards easing symptoms associated with neurological illnesses, yet poor diagnosis and imprecise understanding of these disorders has led to imperfect treatment options. The scenario is complicated by the inability to extrapolate results of cell culture studies and transgenic models to clinical applications which has stagnated the process of improving drug therapy. In this context, the development of biomarkers has been viewed as beneficial to easing various pathological complications. A biomarker is measured and evaluated in order to gauge the physiological process or a pathological progression of a disease and such a marker can also indicate the clinical or pharmacological response to a therapeutic intervention. The development and identification of biomarkers for neurological disorders involves several issues including the complexity of the brain, unresolved discrepant data from experimental and clinical studies, poor clinical diagnostics, lack of functional endpoints, and high cost and complexity of techniques yet research in the area of biomarkers is highly desired. The present work describes existing biomarkers for various neurological disorders, provides support for the idea that biomarker development may ease our understanding underlying pathophysiology of these disorders and help to design and explore therapeutic targets for effective intervention.
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Affiliation(s)
- Vivek Kumar Sharma
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, 140401, India.,Government College of Pharmacy, Rohru, Shimla, Himachal Pradesh, 171207, India
| | - Thakur Gurjeet Singh
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, 140401, India.
| | - Vineet Mehta
- Government College of Pharmacy, Rohru, Shimla, Himachal Pradesh, 171207, India
| | - Ashi Mannan
- Chitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, 140401, India
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25
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Kouba T, Frank W, Tykalova T, Mühlbäck A, Klempíř J, Lindenberg KS, Landwehrmeyer GB, Rusz J. Speech biomarkers in Huntington's disease: A cross-sectional study in pre-symptomatic, prodromal and early manifest stages. Eur J Neurol 2023; 30:1262-1271. [PMID: 36732902 DOI: 10.1111/ene.15726] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/25/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND AND PURPOSE Motor speech alterations are a prominent feature of clinically manifest Huntington's disease (HD). Objective acoustic analysis of speech can quantify speech alterations. It is currently unknown, however, at what stage of HD speech alterations can be reliably detected. We aimed to explore the patterns and extent of speech alterations using objective acoustic analysis in HD and to assess correlations with both rater-assessed phenotypical features and biological determinants of HD. METHODS Speech samples were acquired from 44 premanifest (29 pre-symptomatic and 15 prodromal) and 25 manifest HD gene expansion carriers, and 25 matched healthy controls. A quantitative automated acoustic analysis of 10 speech dimensions was performed. RESULTS Automated speech analysis allowed us to differentiate between participants with HD and controls, with areas under the curve of 0.74 for pre-symptomatic, 0.92 for prodromal, and 0.97 for manifest stages. In addition to irregular alternating motion rates and prolonged pauses seen only in manifest HD, both prodromal and manifest HD displayed slowed articulation rate, slowed alternating motion rates, increased loudness variability, and unstable steady-state position of articulators. In participants with premanifest HD, speech alteration severity was associated with cognitive slowing (r = -0.52, p < 0.001) and the extent of bradykinesia (r = 0.43, p = 0.004). Speech alterations correlated with a measure of exposure to mutant gene products (CAG-age-product score; r = 0.60, p < 0.001). CONCLUSION Speech abnormalities in HD are associated with other motor and cognitive deficits and are measurable already in premanifest stages of HD. Therefore, automated speech analysis might represent a quantitative HD biomarker with potential for assessing disease progression.
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Affiliation(s)
- Tomas Kouba
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Wiebke Frank
- Department of Neurology, University Ulm, Ulm, Germany
| | - Tereza Tykalova
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Alzbeta Mühlbäck
- Department of Neurology, University Ulm, Ulm, Germany.,Department of Neuropsychiatry, Huntington Center South, kbo-Isar-Amper-Klinikum Taufkirchen (Vils), Taufkirchen, Germany.,Department of Neurology and Center of Clinical Neuroscience, 1st Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jiří Klempíř
- Department of Neurology and Center of Clinical Neuroscience, 1st Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | | | | | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.,Department of Neurology and Center of Clinical Neuroscience, 1st Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.,Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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26
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Pinto S, Nebel A, Rau J, Espesser R, Maillochon P, Niebuhr O, Krack P, Witjas T, Ghio A, Cuartero MC, Timmermann L, Schnitzler A, Hesekamp H, Meier N, Müllner J, Hälbig TD, Möller B, Paschen S, Paschen L, Volkmann J, Barbe MT, Fink GR, Becker J, Reker P, Kühn AA, Schneider GH, Fraix V, Seigneuret E, Kistner A, Rascol O, Brefel-Courbon C, Ory-Magne F, Hartmann CJ, Wojtecki L, Fradet A, Maltête D, Damier P, Le Dily S, Sixel-Döring F, Benecke P, Weiss D, Wächter T, Pinsker MO, Régis J, Thobois S, Polo G, Houeto JL, Hartmann A, Knudsen K, Vidailhet M, Schüpbach M, Deuschl G. Results of a Randomized Clinical Trial of Speech After Early Neurostimulation in Parkinson's Disease. Mov Disord 2023; 38:212-222. [PMID: 36461899 DOI: 10.1002/mds.29282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/03/2022] [Accepted: 11/06/2022] [Indexed: 12/07/2022] Open
Abstract
BACKGROUND The EARLYSTIM trial demonstrated for Parkinson's disease patients with early motor complications that deep brain stimulation of the subthalamic nucleus (STN-DBS) and best medical treatment (BMT) was superior to BMT alone. OBJECTIVE This prospective, ancillary study on EARLYSTIM compared changes in blinded speech intelligibility assessment between STN-DBS and BMT over 2 years, and secondary outcomes included non-speech oral movements (maximum phonation time [MPT], oral diadochokinesis), physician- and patient-reported assessments. METHODS STN-DBS (n = 102) and BMT (n = 99) groups underwent assessments on/off medication at baseline and 24 months (in four conditions: on/off medication, ON/OFF stimulation-for STN-DBS). Words and sentences were randomly presented to blinded listeners, and speech intelligibility rate was measured. Statistical analyses compared changes between the STN-DBS and BMT groups from baseline to 24 months. RESULTS Over the 2-year period, changes in speech intelligibility and MPT, as well as patient-reported outcomes, were not different between groups, either off or on medication or OFF or ON stimulation, but most outcomes showed a nonsignificant trend toward worsening in both groups. Change in oral diadochokinesis was significantly different between STN-DBS and BMT groups, on medication and OFF STN-DBS, with patients in the STN-DBS group performing slightly worse than patients under BMT only. A signal for clinical worsening with STN-DBS was found for the individual speech item of the Unified Parkinson's Disease Rating Scale, Part III. CONCLUSION At this early stage of the patients' disease, STN-DBS did not result in a consistent deterioration in blinded speech intelligibility assessment and patient-reported communication, as observed in studies of advanced Parkinson's Disease. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Serge Pinto
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
| | - Adelheid Nebel
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Jörn Rau
- Coordinating Centre for Clinical Trials of the Philipps-University of Marburg, Marburg, Germany
| | | | | | - Oliver Niebuhr
- Department of Scandinavian Studies, Frisian, and General Linguistics, Kiel University, Kiel, Germany
| | - Paul Krack
- Department of Neurology or Neurosurgery, Grenoble University Hospital, Grenoble Alpes University, Grenoble Institut des Neurosciences, Grenoble, France
| | - Tatiana Witjas
- Aix Marseille Univ, APHM, La Timone, Neurology Department or Department of Functional and Stereotactic Neurosurgery and Radiosurgery, Marseille, France
| | - Alain Ghio
- Aix-Marseille Univ, CNRS, LPL, Aix-en-Provence, France
| | | | - Lars Timmermann
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Alfons Schnitzler
- Department of Neurology and Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Helke Hesekamp
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Niklaus Meier
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Julia Müllner
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Thomas D Hälbig
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Bettina Möller
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Steffen Paschen
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Laura Paschen
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Jens Volkmann
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Michael T Barbe
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Johannes Becker
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Paul Reker
- Department of Neurology, University Hospital Cologne, Cologne, Germany
| | - Andrea A Kühn
- Department of Neurology, Charité Hospital, Berlin University, Berlin, Germany
| | | | - Valérie Fraix
- Department of Neurology or Neurosurgery, Grenoble University Hospital, Grenoble Alpes University, Grenoble Institut des Neurosciences, Grenoble, France
| | - Eric Seigneuret
- Department of Neurology or Neurosurgery, Grenoble University Hospital, Grenoble Alpes University, Grenoble Institut des Neurosciences, Grenoble, France
| | - Andrea Kistner
- Department of Neurology or Neurosurgery, Grenoble University Hospital, Grenoble Alpes University, Grenoble Institut des Neurosciences, Grenoble, France
| | - Olivier Rascol
- Department of Neurology and Centre Expert Parkinson, and INSERM U1214, Toulouse University Hospital, Toulouse NeuroImaging Centre, Toulouse, France
| | - Christine Brefel-Courbon
- Department of Neurology and Centre Expert Parkinson, and INSERM U1214, Toulouse University Hospital, Toulouse NeuroImaging Centre, Toulouse, France
| | - Fabienne Ory-Magne
- Department of Neurology and Centre Expert Parkinson, and INSERM U1214, Toulouse University Hospital, Toulouse NeuroImaging Centre, Toulouse, France
| | - Christian J Hartmann
- Department of Neurology and Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Lars Wojtecki
- Department of Neurology and Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Anne Fradet
- Department of Neurology, CIC-INSERM 1402, CHU de Poitiers, Université de Poitiers, Poitiers, France
| | - David Maltête
- Department of Neurology, Rouen University Hospital, INSERM U1073, Rouen Faculty of Medicine, Rouen, France
| | - Philippe Damier
- CHU Nantes, INSERM, CIC1413, Hôpital Laënnec, Nantes, France
| | | | | | - Petra Benecke
- Department of Neurology, Paracelsus-Elena-Klinik, Kassel, Germany
| | - Daniel Weiss
- Department for Neurodegenerative Diseases, Centre for Neurology, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Tobias Wächter
- Department for Neurodegenerative Diseases, Centre for Neurology, and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Marcus O Pinsker
- Department of Neurosurgery, University Hospital, Freiburg, Germany
| | - Jean Régis
- Aix Marseille Univ, APHM, La Timone, Neurology Department or Department of Functional and Stereotactic Neurosurgery and Radiosurgery, Marseille, France
| | - Stéphane Thobois
- Hôpital Neurologique Pierre Wertheimer, Centre Expert Parkinson, Hospices Civils de Lyon, Université Claude Bernard Lyon 1 Lyon, France, and Centre de Neurosciences Cognitives, Bron, France
| | - Gustavo Polo
- Hôpital Neurologique Pierre Wertheimer, Centre Expert Parkinson, Hospices Civils de Lyon, Université Claude Bernard Lyon 1 Lyon, France, and Centre de Neurosciences Cognitives, Bron, France
| | - Jean-Luc Houeto
- Department of Neurology, CIC-INSERM 1402, CHU de Poitiers, Université de Poitiers, Poitiers, France
| | - Andreas Hartmann
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Karina Knudsen
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
| | - Marie Vidailhet
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Michael Schüpbach
- Department of Neurology, Pitié-Salpêtrière Hospital, Assistance Publique Hôpitaux de Paris (APHP), INSERM, Institut du Cerveau et de la Moelle Epinière, and Centre d'Investigation Clinique (CIC), Paris, France
| | - Günther Deuschl
- Department of Neurology, University Medical Center Schleswig-Holstein, Christian-Albrechts-University, Kiel, Germany
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Favaro A, Moro-Velázquez L, Butala A, Motley C, Cao T, Stevens RD, Villalba J, Dehak N. Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease. Front Neurol 2023; 14:1142642. [PMID: 36937510 PMCID: PMC10017962 DOI: 10.3389/fneur.2023.1142642] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/08/2023] [Indexed: 03/06/2023] Open
Abstract
Motor impairments are only one aspect of Parkinson's disease (PD), which also include cognitive and linguistic impairments. Speech-derived interpretable biomarkers may help clinicians diagnose PD at earlier stages and monitor the disorder's evolution over time. This study focuses on the multilingual evaluation of a composite array of biomarkers that facilitate PD evaluation from speech. Hypokinetic dysarthria, a motor speech disorder associated with PD, has been extensively analyzed in previously published studies on automatic PD evaluation, with a relative lack of inquiry into language and task variability. In this study, we explore certain acoustic, linguistic, and cognitive information encoded within the speech of several cohorts with PD. A total of 24 biomarkers were analyzed from American English, Italian, Castilian Spanish, Colombian Spanish, German, and Czech by conducting a statistical analysis to evaluate which biomarkers best differentiate people with PD from healthy participants. The study leverages conceptual robustness as a criterion in which a biomarker behaves the same, independent of the language. Hence, we propose a set of speech-based biomarkers that can effectively help evaluate PD while being language-independent. In short, the best acoustic and cognitive biomarkers permitting discrimination between experimental groups across languages were fundamental frequency standard deviation, pause time, pause percentage, silence duration, and speech rhythm standard deviation. Linguistic biomarkers representing the length of the narratives and the number of nouns and auxiliaries also provided discrimination between groups. Altogether, in addition to being significant, these biomarkers satisfied the robustness requirements.
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Affiliation(s)
- Anna Favaro
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States
- *Correspondence: Anna Favaro
| | - Laureano Moro-Velázquez
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States
| | - Ankur Butala
- Department of Neurology, The Johns Hopkins University, Baltimore, MD, United States
- Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University, Baltimore, MD, United States
| | - Chelsie Motley
- Department of Neurology, The Johns Hopkins University, Baltimore, MD, United States
| | - Tianyu Cao
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States
| | - Robert David Stevens
- Department of Anesthesiology and Critical Care, The Johns Hopkins University, Baltimore, MD, United States
| | - Jesús Villalba
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States
| | - Najim Dehak
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, United States
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Svihlik J, Novotny M, Tykalova T, Polakova K, Brozova H, Kryze P, Sousa M, Krack P, Tripoliti E, Ruzicka E, Jech R, Rusz J. Long-Term Averaged Spectrum Descriptors of Dysarthria in Patients With Parkinson's Disease Treated With Subthalamic Nucleus Deep Brain Stimulation. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2022; 65:4690-4699. [PMID: 36472939 DOI: 10.1044/2022_jslhr-22-00308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
PURPOSE This study aimed to evaluate whether long-term averaged spectrum (LTAS) descriptors for reading and monologue are suitable to detect worsening of dysarthria in patients with Parkinson's disease (PD) treated with subthalamic nucleus deep brain stimulation (STN-DBS) with potential effect of ON and OFF stimulation conditions and types of connected speech. METHOD Four spectral moments based on LTAS were computed for monologue and reading passage collected from 23 individuals with PD treated with bilateral STN-DBS and 23 age- and gender-matched healthy controls. Speech performance of patients with PD was compared in ON and OFF STN-DBS conditions. RESULTS All LTAS spectral moments including mean, standard deviation, skewness, and kurtosis across both monologue and reading passage were able to significantly distinguish between patients with PD in both stimulation conditions and control speakers. The spectral mean was the only LTAS measure sensitive to capture better speech performance in STN-DBS ON, as compared to the STN-DBS OFF stimulation condition (p < .05). Standardized reading passage was more sensitive compared to monologue in detecting dysarthria severity via LTAS descriptors with an area under the curve of up to 0.92 obtained between PD and control groups. CONCLUSIONS Our findings confirmed that LTAS is a suitable approach to objectively describe changes in speech impairment severity due to STN-DBS therapy in patients with PD. We envisage these results as an important step toward a continuum development of technological solutions for the automated assessment of stimulation-induced dysarthria. SUPPLEMENTAL MATERIAL https://doi.org/10.23641/asha.21644798.
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Affiliation(s)
- Jan Svihlik
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
- Department of Computing and Control Engineering, Faculty of Chemical Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Michal Novotny
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Tereza Tykalova
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Kamila Polakova
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital, Prague, Czech Republic
| | - Hana Brozova
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital, Prague, Czech Republic
| | - Petr Kryze
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Mario Sousa
- Movement Disorders Center, Department of Neurology, University Hospital of Bern, Switzerland
| | - Paul Krack
- Movement Disorders Center, Department of Neurology, University Hospital of Bern, Switzerland
| | - Elina Tripoliti
- UCL Queen Square Institute of Neurology, Department of Clinical and Movement Neurosciences, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, United Kingdom
| | - Evzen Ruzicka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital, Prague, Czech Republic
| | - Robert Jech
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, and General University Hospital, Prague, Czech Republic
- Movement Disorders Center, Department of Neurology, University Hospital of Bern, Switzerland
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Aplicaciones de aprendizaje automático en salud. REVISTA MÉDICA CLÍNICA LAS CONDES 2022. [DOI: 10.1016/j.rmclc.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Ngo QC, Motin MA, Pah ND, Drotár P, Kempster P, Kumar D. Computerized analysis of speech and voice for Parkinson's disease: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107133. [PMID: 36183641 DOI: 10.1016/j.cmpb.2022.107133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE Speech impairment is an early symptom of Parkinson's disease (PD). This study has summarized the literature related to speech and voice in detecting PD and assessing its severity. METHODS A systematic review of the literature from 2010 to 2021 to investigate analysis methods and signal features. The keywords "Automatic analysis" in conjunction with "PD speech" or "PD voice" were used, and the PubMed and ScienceDirect databases were searched. A total of 838 papers were found on the first run, of which 189 were selected. One hundred and forty-seven were found to be suitable for the review. The different datasets, recording protocols, signal analysis methods and features that were reported are listed. Values of the features that separate PD patients from healthy controls were tabulated. Finally, the barriers that limit the wide use of computerized speech analysis are discussed. RESULTS Speech and voice may be valuable markers for PD. However, large differences between the datasets make it difficult to compare different studies. In addition, speech analytic methods that are not informed by physiological understanding may alienate clinicians. CONCLUSIONS The potential usefulness of speech and voice for the detection and assessment of PD is confirmed by evidence from the classification and correlation results.
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Affiliation(s)
| | - Mohammod Abdul Motin
- Biosignals Lab, RMIT University, Melbourne, Australia; Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Nemuel Daniel Pah
- Biosignals Lab, RMIT University, Melbourne, Australia; Universitas Surabaya, Indonesia
| | - Peter Drotár
- Intelligent Information Systems Lab, Technical University of Kosice, Letna 9, 42001, Kosice, Slovakia
| | - Peter Kempster
- Neurosciences Department, Monash Health, Clayton, VIC, Australia; Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Dinesh Kumar
- Biosignals Lab, RMIT University, Melbourne, Australia.
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Speech acoustic indices for differential diagnosis between Parkinson's disease, multiple system atrophy and progressive supranuclear palsy. NPJ Parkinsons Dis 2022; 8:142. [PMID: 36302780 PMCID: PMC9613976 DOI: 10.1038/s41531-022-00389-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 09/01/2022] [Indexed: 11/05/2022] Open
Abstract
While speech disorder represents an early and prominent clinical feature of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP), little is known about the sensitivity of speech assessment as a potential diagnostic tool. Speech samples were acquired from 215 subjects, including 25 MSA, 20 PSP, 20 Parkinson's disease participants, and 150 healthy controls. The accurate differential diagnosis of dysarthria subtypes was based on the quantitative acoustic analysis of 26 speech dimensions related to phonation, articulation, prosody, and timing. A semi-supervised weighting-based approach was then applied to find the best feature combinations for separation between PSP and MSA. Dysarthria was perceptible in all PSP and MSA patients and consisted of a combination of hypokinetic, spastic, and ataxic components. Speech features related to respiratory dysfunction, imprecise consonants, monopitch, slow speaking rate, and subharmonics contributed to worse performance in PSP than MSA, whereas phonatory instability, timing abnormalities, and articulatory decay were more distinctive for MSA compared to PSP. The combination of distinct speech patterns via objective acoustic evaluation was able to discriminate between PSP and MSA with very high accuracy of up to 89% as well as between PSP/MSA and PD with up to 87%. Dysarthria severity in MSA/PSP was related to overall disease severity. Speech disorders reflect the differing underlying pathophysiology of tauopathy in PSP and α-synucleinopathy in MSA. Vocal assessment may provide a low-cost alternative screening method to existing subjective clinical assessment and imaging diagnostic approaches.
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Skrabal D, Rusz J, Novotny M, Sonka K, Ruzicka E, Dusek P, Tykalova T. Articulatory undershoot of vowels in isolated REM sleep behavior disorder and early Parkinson's disease. NPJ Parkinsons Dis 2022; 8:137. [PMID: 36266347 PMCID: PMC9584921 DOI: 10.1038/s41531-022-00407-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/04/2022] [Indexed: 11/09/2022] Open
Abstract
Imprecise vowels represent a common deficit associated with hypokinetic dysarthria resulting from a reduced articulatory range of motion in Parkinson's disease (PD). It is not yet unknown whether the vowel articulation impairment is already evident in the prodromal stages of synucleinopathy. We aimed to assess whether vowel articulation abnormalities are present in isolated rapid eye movement sleep behaviour disorder (iRBD) and early-stage PD. A total of 180 male participants, including 60 iRBD, 60 de-novo PD and 60 age-matched healthy controls performed reading of a standardized passage. The first and second formant frequencies of the corner vowels /a/, /i/, and /u/ extracted from predefined words, were utilized to construct articulatory-acoustic measures of Vowel Space Area (VSA) and Vowel Articulation Index (VAI). Compared to controls, VSA was smaller in both iRBD (p = 0.01) and PD (p = 0.001) while VAI was lower only in PD (p = 0.002). iRBD subgroup with abnormal olfactory function had smaller VSA compared to iRBD subgroup with preserved olfactory function (p = 0.02). In PD patients, the extent of bradykinesia and rigidity correlated with VSA (r = -0.33, p = 0.01), while no correlation between axial gait symptoms or tremor and vowel articulation was detected. Vowel articulation impairment represents an early prodromal symptom in the disease process of synucleinopathy. Acoustic assessment of vowel articulation may provide a surrogate marker of synucleinopathy in scenarios where a single robust feature to monitor the dysarthria progression is needed.
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Affiliation(s)
- Dominik Skrabal
- grid.411798.20000 0000 9100 9940Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- grid.411798.20000 0000 9100 9940Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic ,grid.6652.70000000121738213Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic ,grid.5734.50000 0001 0726 5157Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Michal Novotny
- grid.6652.70000000121738213Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Karel Sonka
- grid.411798.20000 0000 9100 9940Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Evzen Ruzicka
- grid.411798.20000 0000 9100 9940Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petr Dusek
- grid.411798.20000 0000 9100 9940Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Tereza Tykalova
- grid.6652.70000000121738213Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
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Macklin EA, Coffey CS, Brumm MC, Seibyl JP. Statistical Considerations in the Design of Clinical Trials Targeting Prodromal Parkinson Disease. Neurology 2022; 99:68-75. [PMID: 35970588 DOI: 10.1212/wnl.0000000000200897] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/13/2022] [Indexed: 11/15/2022] Open
Abstract
Clinical trials testing interventions for prodromal Parkinson disease (PD) hold particular promise for preserving neuronal function and thereby slowing or even forestalling progression to overt PD. Selection of the appropriate target population and outcome measures presents challenges unique to prodromal PD. We propose 3 clinical trial designs, spanning phase 2a, phase 2b, and phase 3 development, that might serve as templates for prodromal PD trials. The proposed phase 2a trial is of a 3-arm design of short duration and focuses on proof of concept with respect to target engagement and change in a motor outcome in a subset of prodromal participants who already manifest asymptomatic but measurable motor dysfunction as an exploratory aim. The proposed phase 2b trial suggests progression of dopamine transporter imaging specific binding ratio as a primary outcome evaluated annually over 2 years with phenoconversion to PD as a key secondary outcome. The proposed phase 3 trial is a large, simple design of a nutraceutical or behavioral intervention with remote administration and phenoconversion as the primary outcome. We then consider what additional data are needed in the short term to better design prodromal PD trials and examine what longer-term goals would accelerate discovery of safe and effective therapies for individuals at risk of PD. Clear and potentially context-specific definitions of phenoconversion and validation of intermediate endpoints are needed in the short term. The use of adaptive trial designs, master protocols, and research registries would help accelerate therapy development in the long term.
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Affiliation(s)
- Eric A Macklin
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT.
| | - Christopher S Coffey
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
| | - Michael C Brumm
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
| | - John Peter Seibyl
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
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Svoboda E, Bořil T, Rusz J, Tykalová T, Horáková D, Guttmann CRG, Blagoev KB, Hatabu H, Valtchinov VI. Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study. Comput Biol Med 2022; 148:105853. [PMID: 35870318 DOI: 10.1016/j.compbiomed.2022.105853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/09/2022] [Accepted: 05/23/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria. METHOD The objective was to determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-the-curve. RESULTS The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-the-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant. CONCLUSION Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding multiple sclerosis diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.
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Affiliation(s)
- E Svoboda
- Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Institute of Phonetics, Faculty of Arts, Charles University, Prague, Czech Republic
| | - T Bořil
- Institute of Phonetics, Faculty of Arts, Charles University, Prague, Czech Republic
| | - J Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic; Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; Department of Neurology & ARTORG Center, Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - T Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - D Horáková
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - C R G Guttmann
- Center for Neurological Imaging, Brigham & Women's Hospital and Harvard Medical School, USA
| | - K B Blagoev
- Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - H Hatabu
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - V I Valtchinov
- Center for Evidence-Based Imaging, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Khaskhoussy R, Ayed YB. Speech processing for early Parkinson’s disease diagnosis: machine learning and deep learning-based approach. SOCIAL NETWORK ANALYSIS AND MINING 2022. [DOI: 10.1007/s13278-022-00905-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kouba T, Illner V, Rusz J. Study protocol for using a smartphone application to investigate speech biomarkers of Parkinson's disease and other synucleinopathies: SMARTSPEECH. BMJ Open 2022; 12:e059871. [PMID: 35772829 PMCID: PMC9247696 DOI: 10.1136/bmjopen-2021-059871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
INTRODUCTION Early identification of Parkinson's disease (PD) in its prodromal stage has fundamental implications for the future development of neuroprotective therapies. However, no sufficiently accurate biomarkers of prodromal PD are currently available to facilitate early identification. The vocal assessment of patients with isolated rapid eye movement sleep behaviour disorder (iRBD) and PD appears to have intriguing potential as a diagnostic and progressive biomarker of PD and related synucleinopathies. METHODS AND ANALYSIS Speech patterns in the spontaneous speech of iRBD, early PD and control participants' voice calls will be collected from data acquired via a developed smartphone application over a period of 2 years. A significant increase in several aspects of PD-related speech disorders is expected, and is anticipated to reflect the underlying neurodegeneration processes. ETHICS AND DISSEMINATION The study has been approved by the Ethics Committee of the General University Hospital in Prague, Czech Republic and all the participants will provide written, informed consent prior to their inclusion in the research. The application satisfies the General Data Protection Regulation law requirements of the European Union. The study findings will be published in peer-reviewed journals and presented at international scientific conferences.
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Affiliation(s)
- Tomáš Kouba
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Vojtěch Illner
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
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37
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Senturk ZK. Layer recurrent neural network-based diagnosis of Parkinson’s disease using voice features. BIOMED ENG-BIOMED TE 2022; 67:249-266. [DOI: 10.1515/bmt-2022-0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 05/18/2022] [Indexed: 12/13/2022]
Abstract
Abstract
Parkinson’s disease (PD), a slow-progressing neurological disease, affects a large percentage of the world’s elderly population, and this population is expected to grow over the next decade. As a result, early detection is crucial for community health and the future of the globe in order to take proper safeguards and have a less arduous treatment procedure. Recent research has begun to focus on the motor system deficits caused by PD. Because practically most of the PD patients suffer from voice abnormalities, researchers working on automated diagnostic systems investigate vocal impairments. In this paper, we undertake extensive experiments with features extracted from voice signals. We propose a layer Recurrent Neural Network (RNN) based diagnosis for PD. To prove the efficiency of the model, different network models are compared. To the best of our knowledge, several neural network topologies, namely RNN, Cascade Forward Neural Networks (CFNN), and Feed Forward Neural Networks (FFNN), are used and compared for voice-based PD detection for the first time. In addition, the impacts of data normalization and feature selection (FS) are thoroughly examined. The findings reveal that normalization increases classifier performance and Laplacian-based FS outperforms. The proposed RNN model with 300 voice features achieves 99.74% accuracy.
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Affiliation(s)
- Zehra Karapinar Senturk
- Computer Engineering Department , Faculty of Engineering, Duzce University , 81620 , Duzce , Turkey
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38
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Riad R, Lunven M, Titeux H, Cao XN, Hamet Bagnou J, Lemoine L, Montillot J, Sliwinski A, Youssov K, Cleret de Langavant L, Dupoux E, Bachoud-Lévi AC. Predicting clinical scores in Huntington's disease: a lightweight speech test. J Neurol 2022; 269:5008-5021. [PMID: 35567614 PMCID: PMC9363375 DOI: 10.1007/s00415-022-11148-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 01/19/2022] [Accepted: 04/18/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVES Using brief samples of speech recordings, we aimed at predicting, through machine learning, the clinical performance in Huntington's Disease (HD), an inherited Neurodegenerative disease (NDD). METHODS We collected and analyzed 126 samples of audio recordings of both forward and backward counting from 103 Huntington's disease gene carriers [87 manifest and 16 premanifest; mean age 50.6 (SD 11.2), range (27-88) years] from three multicenter prospective studies in France and Belgium (MIG-HD (ClinicalTrials.gov NCT00190450); BIO-HD (ClinicalTrials.gov NCT00190450) and Repair-HD (ClinicalTrials.gov NCT00190450). We pre-registered all of our methods before running any analyses, in order to avoid inflated results. We automatically extracted 60 speech features from blindly annotated samples. We used machine learning models to combine multiple speech features in order to make predictions at individual levels of the clinical markers. We trained machine learning models on 86% of the samples, the remaining 14% constituted the independent test set. We combined speech features with demographics variables (age, sex, CAG repeats, and burden score) to predict cognitive, motor, and functional scores of the Unified Huntington's disease rating scale. We provided correlation between speech variables and striatal volumes. RESULTS Speech features combined with demographics allowed the prediction of the individual cognitive, motor, and functional scores with a relative error from 12.7 to 20.0% which is better than predictions using demographics and genetic information. Both mean and standard deviation of pause durations during backward recitation and clinical scores correlated with striatal atrophy (Spearman 0.6 and 0.5-0.6, respectively). INTERPRETATION Brief and examiner-free speech recording and analysis may become in the future an efficient method for remote evaluation of the individual condition in HD and likely in other NDD.
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Affiliation(s)
- Rachid Riad
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France.,Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Marine Lunven
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Hadrien Titeux
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Xuan-Nga Cao
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Jennifer Hamet Bagnou
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Laurie Lemoine
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Justine Montillot
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Agnes Sliwinski
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Katia Youssov
- Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Laurent Cleret de Langavant
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France.,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France.,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France.,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France
| | - Emmanuel Dupoux
- Laboratoire de Sciences Cognitives et Psycholinguistique, CNRS 8554, PSL University, 29 rue d'Ulm, 75005, Paris, France.,INRIA, Cognitive Machine Learning Team, 2 Rue Simone IFF, 75012, Paris, France.,EHESS, 54 boulevard Raspail, 75006, Paris, France
| | - Anne-Catherine Bachoud-Lévi
- Département d'Études Cognitives, École Normale Supérieure, PSL University, 75005, Paris, France. .,Faculté de Médecine, Université Paris-Est Créteil, 94000, Créteil, France. .,Inserm U955, Institut Mondor de Recherche Biomédicale, Équipe E01 NeuroPsychologie Interventionnelle, 94000, Créteil, France. .,Centre de Référence Maladie de Huntington, Service de Neurologie, AP-HP, Hôpital Henri Mondor-Albert Chenevier, 51 avenue du Maréchal de Lattre de Tassigny, 94000, Créteil, France.
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Chan JCS, Stout JC, Shirbin CA, Vogel AP. Listener Detection of Objectively Validated Acoustic Features of Speech in Huntington's Disease. J Huntingtons Dis 2022; 11:71-79. [PMID: 34974436 DOI: 10.3233/jhd-210501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Subtle progressive changes in speech motor function and cognition begin prior to diagnosis of Huntington's disease (HD). OBJECTIVE To determine the nature of listener-rated speech differences in premanifest and early-stage HD (i.e., PreHD and EarlyHD), compared to neurologically healthy controls. METHODS We administered a speech battery to 60 adults (16 people with PreHD, 14 with EarlyHD, and 30 neurologically healthy controls), and conducted a cognitive test of processing speed/visual attention, the Symbol Digit Modalities Test (SDMT) on participants with HD. Voice recordings were rated by expert listeners and analyzed for acoustic and perceptual speech features. RESULTS Listeners perceived subtle differences in the speech of PreHD compared to controls, including abnormal pitch level and speech rate, reduced loudness and loudness inflection, altered voice quality, hypernasality, imprecise articulation, and reduced naturalness of speech. Listeners detected abnormal speech rate in PreHD compared to healthy speakers on a reading task, which correlated with slower speech rate from acoustic analysis and a lower cognitive performance score. In early-stage HD, continuous speech was characterized by longer pauses, a higher proportion of silence, and slower rate. CONCLUSION Differences in speech and voice acoustic features are detectable in PreHD by expert listeners and align with some acoustically-derived objective speech measures. Slower speech rate in PreHD suggests altered oral motor control and/or subtle cognitive deficits that begin prior to diagnosis. Speakers with EarlyHD exhibited more silences compared to the PreHD and control groups, raising the likelihood of a link between speech and cognition that is not yet well characterized in HD.
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Affiliation(s)
- Jess C S Chan
- Centre for Neuroscience of Speech, University of Melbourne, Victoria, Australia
| | - Julie C Stout
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Christopher A Shirbin
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Adam P Vogel
- Centre for Neuroscience of Speech, University of Melbourne, Victoria, Australia.,Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Germany & Center for Neurology, University Hospital Tübingen, Germany.,Redenlab, Australia
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Illner V, Tykalová T, Novotný M, Klempíř J, Dušek P, Rusz J. Toward Automated Articulation Rate Analysis via Connected Speech in Dysarthrias. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2022; 65:1386-1401. [PMID: 35302874 DOI: 10.1044/2021_jslhr-21-00549] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
PURPOSE This study aimed to evaluate the reliability of different approaches for estimating the articulation rates in connected speech of Parkinsonian patients with different stages of neurodegeneration compared to healthy controls. METHOD Monologues and reading passages were obtained from 25 patients with idiopathic rapid eye movement sleep behavior disorder (iRBD), 25 de novo patients with Parkinson's disease (PD), 20 patients with multiple system atrophy (MSA), and 20 healthy controls. The recordings were subsequently evaluated using eight syllable localization algorithms, and their performances were compared to a manual transcript used as a reference. RESULTS The Google & Pyphen method, based on automatic speech recognition followed by hyphenation, outperformed the other approaches (automated vs. hand transcription: r > .87 for monologues and r > .91 for reading passages, p < .001) in precise feature estimates and resilience to dysarthric speech. The Praat script algorithm achieved sufficient robustness (automated vs. hand transcription: r > .65 for monologues and r > .78 for reading passages, p < .001). Compared to the control group, we detected a slow rate in patients with MSA and a tendency toward a slower rate in patients with iRBD, whereas the articulation rate was unchanged in patients with early untreated PD. CONCLUSIONS The state-of-the-art speech recognition tool provided the most precise articulation rate estimates. If speech recognizer is not accessible, the freely available Praat script based on simple intensity thresholding might still provide robust properties even in severe dysarthria. Automated articulation rate assessment may serve as a natural, inexpensive biomarker for monitoring disease severity and a differential diagnosis of Parkinsonism.
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Affiliation(s)
- Vojtěch Illner
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Jiří Klempíř
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
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Karan B, Sahu SS, Orozco-Arroyave JR. An investigation about the relationship between dysarthria level of speech and the neurological state of Parkinson’s patients. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Tykalova T, Novotny M, Ruzicka E, Dusek P, Rusz J. Short-term effect of dopaminergic medication on speech in early-stage Parkinson's disease. NPJ Parkinsons Dis 2022; 8:22. [PMID: 35256614 PMCID: PMC8901688 DOI: 10.1038/s41531-022-00286-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/01/2022] [Indexed: 11/24/2022] Open
Abstract
The effect of dopaminergic medication on speech has rarely been examined in early-stage Parkinson’s disease (PD) and the respective literature is inconclusive and limited by inappropriate design with lack of PD control group. The study aims to examine the short-term effect of dopaminergic medication on speech in PD using patients with good motor responsiveness to levodopa challenge compared to a control group of PD patients with poor motor responsiveness. A total of 60 early-stage PD patients were investigated before (OFF) and after (ON) acute levodopa challenge and compared to 30 age-matched healthy controls. PD patients were categorised into two clinical subgroups (PD responders vs. PD nonresponders) according to the comparison of their motor performance based on movement disorder society-unified Parkinson’s disease rating scale, part III. Seven distinctive parameters of hypokinetic dysarthria were examined using quantitative acoustic analysis. We observed increased monopitch (p > 0.01), aggravated monoloudness (p > 0.05) and longer duration of stop consonants (p > 0.05) in PD compared to healthy controls, confirming the presence of hypokinetic dysarthria in early PD. No speech alterations from OFF to ON state were revealed in any of the two PD groups and speech dimensions investigated including monopitch, monoloudness, imprecise consonants, harsh voice, slow sequential motion rates, articulation rate, or inappropriate silences, although a subgroup of PD responders manifested obvious improvement in motor function after levodopa intake (p > 0.001). Since the short-term usage of levodopa does not easily affect voice and speech performance in PD, speech assessment may provide a medication state-independent motor biomarker of PD.
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Affiliation(s)
- Tereza Tykalova
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| | - Michal Novotny
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Evzen Ruzicka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Petr Dusek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.,Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University, Prague, Czech Republic
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Suppa A, Costantini G, Asci F, Di Leo P, Al-Wardat MS, Di Lazzaro G, Scalise S, Pisani A, Saggio G. Voice in Parkinson's Disease: A Machine Learning Study. Front Neurol 2022; 13:831428. [PMID: 35242101 PMCID: PMC8886162 DOI: 10.3389/fneur.2022.831428] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction Parkinson's disease (PD) is characterized by specific voice disorders collectively termed hypokinetic dysarthria. We here investigated voice changes by using machine learning algorithms, in a large cohort of patients with PD in different stages of the disease, OFF and ON therapy. Methods We investigated 115 patients affected by PD (mean age: 68.2 ± 9.2 years) and 108 age-matched healthy subjects (mean age: 60.2 ± 11.0 years). The PD cohort included 57 early-stage patients (Hoehn &Yahr ≤ 2) who never took L-Dopa for their disease at the time of the study, and 58 mid-advanced-stage patients (Hoehn &Yahr >2) who were chronically-treated with L-Dopa. We clinically evaluated voices using specific subitems of the Unified Parkinson's Disease Rating Scale and the Voice Handicap Index. Voice samples recorded through a high-definition audio recorder underwent machine learning analysis based on the support vector machine classifier. We also calculated the receiver operating characteristic curves to examine the diagnostic accuracy of the analysis and assessed possible clinical-instrumental correlations. Results Voice is abnormal in early-stage PD and as the disease progresses, voice increasingly degradres as demonstrated by high accuracy in the discrimination between healthy subjects and PD patients in the early-stage and mid-advanced-stage. Also, L-dopa therapy improves but not restore voice in PD as shown by high accuracy in the comparison between patients OFF and ON therapy. Finally, for the first time we achieved significant clinical-instrumental correlations by using a new score (LR value) calculated by machine learning. Conclusion Voice is abnormal in early-stage PD, progressively degrades in mid-advanced-stage and can be improved but not restored by L-Dopa. Lastly, machine learning allows tracking disease severity and quantifying the symptomatic effect of L-Dopa on voice parameters with previously unreported high accuracy, thus representing a potential new biomarker of PD.
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Affiliation(s)
- Antonio Suppa
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.,IRCCS Neuromed Institute, Pozzilli, Italy
| | - Giovanni Costantini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | | | - Pietro Di Leo
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | | | - Giulia Di Lazzaro
- Neurology Unit, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Simona Scalise
- Department of System Medicine UOSD Parkinson, University of Rome Tor Vergata, Rome, Italy
| | - Antonio Pisani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,IRCCS Mondino Foundation, Pavia, Italy
| | - Giovanni Saggio
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
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Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi ME, Marín Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Front Neurol 2022; 13:788427. [PMID: 35295840 PMCID: PMC8918525 DOI: 10.3389/fneur.2022.788427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
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Affiliation(s)
- Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Noémi Bontridder
- Centre de Recherches Information, Droit et Societe, University of Namur, Namur, Belgium
| | | | - Enrico Glaab
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | | | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | - Jürgen Winkler
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
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Voice characteristics from isolated rapid eye movement sleep behavior disorder to early Parkinson's disease. Parkinsonism Relat Disord 2022; 95:86-91. [DOI: 10.1016/j.parkreldis.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 12/13/2021] [Accepted: 01/06/2022] [Indexed: 11/23/2022]
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Gong SY, Shen Y, Gu HY, Zhuang S, Fu X, Wang QJ, Mao CJ, Hu H, Dai YP, Liu CF. Generalized EEG Slowing Across Phasic REM Sleep, Not Subjective RBD Severity, Predicts Neurodegeneration in Idiopathic RBD. Nat Sci Sleep 2022; 14:407-418. [PMID: 35299628 PMCID: PMC8923684 DOI: 10.2147/nss.s354063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 02/18/2022] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Idiopathic rapid eye movement sleep behavior disorder (iRBD) is the prodromal marker of α-synuclein degeneration with markedly high predictive value. We aim to evaluate the value of electroencephalography (EEG) data during rapid eye movement (REM) sleep and subjective RBD severity in predicting the conversion to neurodegenerative diseases in iRBD patients. METHODS At the baseline, iRBD patients underwent clinical assessment and video-polysomnography (PSG). Relative spectral power for nine frequency bands during phasic and tonic REM sleep in three regions of interest, slow-to-fast ratios, clinical and PSG variables were estimated and compared between iRBD patients who converted to neurodegenerative diseases (iRBD-C) and iRBD patients who remained disease-free (iRBD-NC). Receiver operating characteristic (ROC) curves evaluated the predictive performance of slow-to-fast ratios, and subjective RBD severity as assessed with RBD Questionnaire-Hong Kong. RESULTS Twenty-two (33.8%) patients eventually developed neurodegenerative diseases. The iRBD-C group showed shorter total sleep time (p < 0.001), lower stage 2 sleep percentage (p = 0.044), more periodic leg-movement-related arousal index (p = 0.004), increased tonic chin electromyelographic activity (p = 0.040) and higher REM density in the third REM episode (p = 0.034) than the iRBD-NC group. EEG spectral power analyses revealed that iRBD phenoconverters showed significantly higher delta and lower alpha power, especially in central and occipital regions during the phasic REM state compared to the iRBD-NC group. Significantly higher slow-to-fast ratios were observed in a more generalized way during the phasic state in the iRBD-C group compared to the iRBD-NC group. ROC analyses of the slowing ratio in occipital areas during phasic REM sleep yielded an area under the curve of 0.749 (p = 0.001), while no significant predictive value of subjective RBD severity was observed. CONCLUSION Our study shows that EEG slowing, especially in a more generalized manner during the phasic period, may be a promising marker in predicting phenoconversion in iRBD, rather than subjective RBD severity.
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Affiliation(s)
- Si-Yi Gong
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Yun Shen
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Han-Ying Gu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Sheng Zhuang
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Xiang Fu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.,Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China
| | - Qiao-Jun Wang
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Cheng-Jie Mao
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Hua Hu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Yong-Ping Dai
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Chun-Feng Liu
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.,Institute of Neuroscience, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.,Department of Neurology, Suqian First Hospital, Suqian, People's Republic of China
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Magacho-Coelho C, Camargo Z, Fernandes Filho J. Dermatoglyphic and acoustic analysis of singing voices: a multiple case preliminary report. REVISTA CEFAC 2022. [DOI: 10.1590/1982-0216/20222426821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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48
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Šimek M, Rusz J. Validation of cepstral peak prominence in assessing early voice changes of Parkinson's disease: Effect of speaking task and ambient noise. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 150:4522. [PMID: 34972306 DOI: 10.1121/10.0009063] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 12/03/2021] [Indexed: 06/14/2023]
Abstract
Although the cepstral peak prominence (CPP) and its variant, the cepstral peak prominence smooth (CPPS), are considered to be robust acoustic measures for the evaluation of dysphonia, whether they are sensitive to capture early voice changes in Parkinson's disease (PD) has not yet been explored. This study aimed to investigate the voice changes via the CPP measures in the idiopathic rapid eye movement sleep behavior disorder (iRBD), a special case of prodromal neurodegeneration, and recently diagnosed and advanced-stage Parkinson's disease (AS-PD) patients using different speaking tasks across noise-free and noisy environments. The sustained vowel phonation, reading of passages, and monologues of 60 early stage untreated PD, 30 advanced-stage Parkinson's disease, 60 iRBD, and 60 healthy control (HC) participants were evaluated. Significant differences were found between the PD groups and controls in sustained phonation via the CPP (p < 0.05) and CPPS (p < 0.01) and the monologue via the CPP (p < 0.01), although neither the CPP nor CPPS measures were sufficiently sensitive to capture the possible prodromal dysphonia in the iRBD. The quality of the CPP and CPPS measures was influenced substantially by the addition of ambient noise. It was anticipated that the CPP measures might serve as a promising digital biomarker in assessing the dysphonia from the early stages of PD.
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Affiliation(s)
- Michal Šimek
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
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Rusz J, Janzen A, Tykalová T, Novotný M, Zogala D, Timmermann L, Růžička E, Šonka K, Dušek P, Oertel W. Dysprosody in Isolated REM Sleep Behavior Disorder with Impaired Olfaction but Intact Nigrostriatal Pathway. Mov Disord 2021; 37:619-623. [PMID: 34837250 DOI: 10.1002/mds.28873] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/01/2021] [Accepted: 11/08/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Impairments of olfactory and speech function are likely early prodromal symptoms of α-synucleinopathy. OBJECTIVE The aim of this study is to assess whether dysprosody is present in isolated rapid eye movement sleep behavior disorder (iRBD) with hyposmia/anosmia and a normal nigrostriatal system. METHODS Pitch variability during speech was investigated in 17 iRBD subjects with normal olfactory function (iRBD-NOF), 30 iRBD subjects with abnormal olfactory function (iRBD-AOF), and 50 healthy controls. iRBD subjects were evaluated using the University of Pennsylvania Smell Identification Test and [123I]-2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)-nortropane dopamine transporter single-photon emission computed tomography (DAT-SPECT). All iRBD subjects completed the 24-month follow-up with DAT-SPECT, speech, and olfactory testing. RESULTS At baseline, only iRBD-AOF showed monopitch when compared to iRBD-NOF (P = 0.04) and controls (P = 0.03), with no difference between iRBD-NOF and controls (P = 1). At follow-up, dysprosody progressed only in iRBD-AOF with abnormal DAT-SPECT (P = 0.03). CONCLUSION Prosody is impaired in hyposmic but not in normosmic iRBD subjects before the nigrostriatal dopaminergic transmission is affected (Braak stage 2). © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jan Rusz
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.,Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Annette Janzen
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Tereza Tykalová
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Michal Novotný
- Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - David Zogala
- Institute of Nuclear Medicine, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czechia
| | - Lars Timmermann
- Department of Neurology, Philipps University Marburg, Marburg, Germany
| | - Evžen Růžička
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Karel Šonka
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petr Dušek
- Department of Neurology and Centre of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Wolfgang Oertel
- Department of Neurology, Philipps University Marburg, Marburg, Germany
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50
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Rusz J, Tykalova T, Novotny M, Zogala D, Sonka K, Ruzicka E, Dusek P. Defining Speech Subtypes in De Novo Parkinson Disease: Response to Long-term Levodopa Therapy. Neurology 2021; 97:e2124-e2135. [PMID: 34607922 DOI: 10.1212/wnl.0000000000012878] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/20/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Patterns of speech disorder in Parkinson disease (PD), which are highly variable across individual patients, have not been systematically studied. Our aim was to identify speech subtypes in treatment-naive patients with PD and to examine their response to long-term dopaminergic therapy. METHODS We recorded speech data from a total of 111 participants with de novo PD; 83 of the participants completed the 12-month follow-up (69 patients with PD on stable dopaminergic medication and 14 untreated controls with PD). Unsupervised k-means cluster analysis was performed on 8 distinctive parameters of hypokinetic dysarthria examined with quantitative acoustic analysis. RESULTS Three distinct speech subtypes with similar prevalence, symptom duration, and motor severity were detected: prosodic, phonatory-prosodic, and articulatory-prosodic. Besides monopitch and monoloudness, which were common in each subtype, speech impairment was more severe in the phonatory-prosodic subtype with predominant dysphonia and the articulatory-prosodic subtype with predominant imprecise consonant articulation than in the prosodic subtype. Clinically, the prosodic subtype was characterized by a prevalence of women and younger age, while articulatory-prosodic subtype was characterized by the prevalence of men, older age, greater severity of axial gait symptoms, and poorer cognitive performance. The phonatory-prosodic subtype clinically represented intermediate status in age with mostly men and preserved cognitive performance. While speech of untreated controls with PD deteriorated over 1 year (p = 0.02), long-term dopaminergic medication maintained stable speech impairment severity in the prosodic and articulatory-prosodic subtypes and improved speech performance in patients with the phonatory-prosodic subtype (p = 0.002). DISCUSSION Distinct speech phenotypes in de novo PD reflect divergent underlying mechanisms and allow prediction of response of speech impairment to levodopa therapy. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that, in patients with newly diagnosed PD with speech impairment, speech phenotype is associated with levodopa responsiveness.
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Affiliation(s)
- Jan Rusz
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic.
| | - Tereza Tykalova
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Michal Novotny
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - David Zogala
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Karel Sonka
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Evzen Ruzicka
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
| | - Petr Dusek
- From the Department of Circuit Theory (J.R., T.T., M.N.), Faculty of Electrical Engineering, Czech Technical University in Prague; Department of Neurology and Centre of Clinical Neuroscience (J.R., K.S., E.R., P.D.), First Faculty of Medicine, Charles University; and Institute of Nuclear Medicine (D.Z.), First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic
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